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MCScheduling 1.0
Set of Algorithms for Solving Mixed-Criticality Scheduling
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MCScheduling 1.0
Set of Algorithms for Solving Mixed-Criticality Scheduling
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| MCScheduling.GeneticAlgorithm.CAlternatingPositionCrossover | The alternating position crossover operator implementation |
| MCScheduling.GeneticAlgorithm.CBaseBreeder | A breeder evolves a population by performing genetic operations defined in the specified configuration of the genetic algorithm |
| MCScheduling.GeneticAlgorithm.CBaseCrossoverOperator | A base class for implementation of a crossover genetic operator |
| MCScheduling.GeneticAlgorithm.CBaseGeneticAlgorithm | A base class for implementation of a genetic algorithm |
| MCScheduling.MixedCriticality.CBaseMixedCriticalitySolver | The basic implementation of the IMixedCriticalitySolver |
| MCScheduling.GeneticAlgorithm.CBaseMutationOperator | A base class for implementation of a mutation genetic operator |
| MCScheduling.GeneticAlgorithm.CBaseSelectionOperator | A base class for implementation of a natural selector |
| MCScheduling.SimulatedAnnealing.CBaseSimulatedAnnealing< T > | The base simulated annealing algorithm |
| MCScheduling.GeneticAlgorithm.CBoltzmannScaling | In case one wants the selection preasure to vary during the evolution he uses Bolzmann scaling |
| MCScheduling.GeneticAlgorithm.CChromosomePool | The basic implementation of the IChromosomePool interface |
| MCScheduling.MixedCriticality.CEDF.CClairvoyantEDFSolver | The mixed-criticality scheduling problem solver based on the clairvoyant non-preemptive EDF scheduling algorithm |
| MCScheduling.GeneticAlgorithm.CConfiguration | The configuration represents a setting of a genetic algorithm, i.e |
| MCScheduling.MixedCriticality.CConsoleSolver | The command line interface for the MCScheduling application |
| MCScheduling.GeneticAlgorithm.CCrossoverOperatorSerializer | A serializer for crossover operators |
| MCScheduling.GeneticAlgorithm.CCycleCrossover | The cycle crossover operator implementation |
| MCScheduling.GeneticAlgorithm.CDefaultBreeder | A default implementation of IBreeder interface |
| MCScheduling.MixedCriticality.CDefaultMixedCriticalityInstanceGenerator | The default implemenation of the IMixedCriticalityInstanceGenerator interface |
| MCScheduling.GeneticAlgorithm.CDeterministicSampling | The deterministic sampling selection operator implementation |
| MCScheduling.GeneticAlgorithm.CDisplacedInversionMutation | The displaced inversion mutation operator |
| MCScheduling.GeneticAlgorithm.CDisplacementMutation | The displacement mutation operator |
| MCScheduling.MixedCriticality.DP.CDPSolver | An dynamic programming type algorithm for solving mixed-criticality scheduling problems of minimal size |
| MCScheduling.GeneticAlgorithm.CExchangeMutation | The exchange mutation operator |
| MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CExponentialAcceptor | Classic exponential distribution acceptor |
| MCScheduling.Utils.CExponentialRandom | A exponential distribution random number generator |
| MCScheduling.GeneticAlgorithm.CFitnessEvaluatorSerializer | The serializer for the implemented finess evalutors |
| MCScheduling.GeneticAlgorithm.CFitnessScalerSerializer | The serializer for the implemented finess scalers |
| MCScheduling.MixedCriticality.GA.CGeneticAlgorithmSolver | The genetic algorihm based solver for the mixed-criticality scheduling |
| MCScheduling.GeneticAlgorithm.CGeneticOperator | The base public class for implementation of a genetic operator |
| MCScheduling.GeneticAlgorithm.CConfiguration.CGreaterFitterComparer | The default fitness comparer |
| MCScheduling.GeneticAlgorithm.CGreedyCrossover | The greedy crossover operator implementation |
| MCScheduling.GeneticAlgorithm.CHeuristicCrossover | The heuristic crossover operator implementation |
| MCScheduling.GeneticAlgorithm.CInsertionMutation | The insertion mutation operator |
| MCScheduling.GeneticAlgorithm.CInversionMutation | The inversion mutation operator |
| MCScheduling.ClairvoyantEDF.ClairvoyantEDF | The mixed-criticality clairvoyant EDF algorithm implementation |
| MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CLowerBetterComparer | Lower energy - lower makespan / lateness - better solution |
| MCScheduling.GeneticAlgorithm.CMersenneTwister | The Mersenne Twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto and Takuji Nishimura |
| MCScheduling.MixedCriticality.GA.CMinimizeMakespanFitnessEvaluator | Evaluates the chromosome representing a schedule for a mixed-criticality instance according to its total lateness and maximum completion time, i.e |
| MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CMinimumMakespanLatenessEvaluator | Evaluates the energy of the given state as a convex sum of the makespan and lateness of the corresponding solution schedule |
| MCScheduling.MixedCriticality.GA.CMixedCriticalityChromosome | The CMixedCriticalityChromosome instance represents a schedule for a mixed-criticality instance that is associated with its genes which should be instances of CMixedCriticalityGene |
| MCScheduling.MixedCriticality.GA.CMixedCriticalityConfiguration | The CMixedCriticalityConfiguration represents a configuration of a genetic algorithm for solving Mixed-criticality Scheduling Problems |
| MCScheduling.MixedCriticality.GA.CMixedCriticalityGene | The mixed-criticality gene represents a place in a schedule for a mixed-criticality job |
| MCScheduling.MixedCriticality.GA.CMixedCriticalityGeneticAlgorithm.CMixedCriticalityGeneDistanceMeasurer | The gene distance measurer for mixed-criticality genetic algorithm |
| MCScheduling.MixedCriticality.GA.CMixedCriticalityGeneticAlgorithm | The implementation of a genetic algorithm to solve mixed-criticality scheduling problems |
| MCScheduling.MixedCriticality.CMixedCriticalityInstance | Representation of a mixed-criticality instance |
| MCScheduling.MixedCriticality.CMixedCriticalityInstancesSerializer | CMixedCriticalityInstancesSerializer provides a methods to serialize and deserialize mixed-criticality instances |
| MCScheduling.MixedCriticality.CMixedCriticalityJob | A CJob instance represents a mixed-criticality job |
| MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing | The simulated annealing solver for the mixed-criticality scheduling |
| MCScheduling.MixedCriticality.MIP.CMixedIntegerProgrammingSolver | The mixed integer programming solver for the mixed-criticality scheduling |
| MCScheduling.GeneticAlgorithm.CMutationOperatorSerializer | A serializer for mutation operators |
| MCScheduling.GeneticAlgorithm.CNullFitnessScaler | A fitness scaler that leaves the fitness value as they are - does not scale |
| MCScheduling.GeneticAlgorithm.COrderBasedCrossover | The order-based crossover operator implementation |
| MCScheduling.GeneticAlgorithm.COrderCrossover | The order crossover operator implementation |
| MCScheduling.GeneticAlgorithm.CPartiallyMappedCrossover | The partially mapped crossover operator implementation |
| MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CPerturber | The perturber used to change one state into one of its "neighbouring" state |
| MCScheduling.GeneticAlgorithm.CPopulation | A population consist of a individual chromosomes that forms potential solutions to a problem |
| MCScheduling.GeneticAlgorithm.CPositionBasedCrossover | The postion-based crossover operator implementation |
| MCScheduling.GeneticAlgorithm.CPriorityQueue< T > | A priority queue implementation based on a binary heap structure, which guarantees that all the operations define has logarithmic time complexity |
| MCScheduling.MixedCriticality.SA.CMixedCriticalitySimulatedAnnealing.CProportionalCooler | The proportional cooler works the way that the temperature at the next time instant is proportional to the temperature in the previous |
| MCScheduling.MixedCriticality.CRandomInstanceGenerator | Generates a list of random MC instances |
| MCScheduling.GeneticAlgorithm.CRandomTournamentSelector | The random tournament selection operator implementation |
| MCScheduling.GeneticAlgorithm.CRankScaling | Rank scaling may be used to prevent too quick convergence |
| MCScheduling.GeneticAlgorithm.CRemainderStochasticSampling | The remainder stochastic sampling selection operator implementation |
| MCScheduling.ClairvoyantEDF.CriticalList | A so-called critical list that is used by the clairvoyant EDF algorithm |
| MCScheduling.GeneticAlgorithm.CRouletteWheelSelector | The roulette wheel selection is one of the standard fitness-proportional selection methods |
| MCScheduling.GeneticAlgorithm.CScrambleMutation | The scramble mutation operator |
| MCScheduling.GeneticAlgorithm.CSelectorOperatorSerializer | A serializer for selector genetic operators |
| MCScheduling.GeneticAlgorithm.CSigmaScaling | Sigma scaling may be used to force a constant selection pressure over many generations |
| MCScheduling.MixedCriticality.SA.CSimulatedAnnealingConfiguration | The CSimulatedAnnealingConfiguration instance stores a configuration of a simulated annealing solver settings |
| MCScheduling.MixedCriticality.SA.CSimulatedAnnealingSolver | The mixed-criticality scheduling problem solver based on the simulated annealing meta-heuristic method |
| MCScheduling.MixedCriticality.SA.CSimulatedAnnealingState | The representation of a solution of an MC instance used by simulated annealing algorithm |
| MCScheduling.SimulatedAnnealing.CBaseSimulatedAnnealing< T >.CState | The state keeps information about the energy of the actual state it wraps |
| MCScheduling.GeneticAlgorithm.CStochasticUniversalSampling | The stochastic universal sampling selector implementation |
| MCScheduling.Utils.CUniformRandom | The (uniform random number generator) Mersenne Twister is a pseudorandom number generator developed in 1997 by Makoto Matsumoto and Takuji Nishimura |
| MCScheduling.MixedCriticality.CWorstCaseMixedCriticalityInstanceGenerator | Generates a random "worst-case" MC instance |
| MCScheduling.ClairvoyantEDF.Task.ExecutionTimeArray | The execution times of the task |
| MCScheduling.GeneticAlgorithm.CPopulation.FittestChromosomeEnumerator | FittestChromosomeEnumerator may be used to enumerate fittest chromosomes of a population in memory efficient way |
| MCScheduling.GanttViewForm | The GanttViewForm is a window whose client are is filled with a char area that display a Gant diagram of an MC instance |
| MCScheduling.SimulatedAnnealing.IAcceptor | The interface of the "probability distribution" of a state being accepted according to its distance from the state from which it has been created |
| MCScheduling.GeneticAlgorithm.IChromosome | Interface providing methods common to all type of chromosomes |
| MCScheduling.GeneticAlgorithm.IChromosomePool | An interface for implementation of a chromosome pool |
| MCScheduling.SimulatedAnnealing.ICloneable< T > | Extended support for cloning, which creates a new instance of a generic class with the same value as an existing instance |
| MCScheduling.SimulatedAnnealing.IComparer | The interface for a comparer of double values |
| MCScheduling.GeneticAlgorithm.IConfigurationDependent | The genetic algorithm components, i.e |
| MCScheduling.SimulatedAnnealing.ICooler | The interface of a temperature cooler that is used to decrease the temperature throughout the optimization process of simulated annealing |
| MCScheduling.GeneticAlgorithm.ICrossoverOperator | Crossover operator is one of the tools of evolution |
| MCScheduling.SimulatedAnnealing.IEvaluator< T > | Defines an evaluator that is used to evaluate states throughout the optimization process of simulated annealing |
| MCScheduling.GeneticAlgorithm.IFitnessComparer | Defines a fitness comparison method for two chromosomes (objects implementing IChromosome interface) |
| MCScheduling.GeneticAlgorithm.IFitnessEvaluator | Defines a fitness calculation method |
| MCScheduling.GeneticAlgorithm.IFitnessScaler | Defines a fitness scaling method |
| MCScheduling.GeneticAlgorithm.IGene | A gene represents a building block from which chromosomes, i.e., solutions to problems are constructed |
| MCScheduling.GeneticAlgorithm.IGeneDistanceMeasurer | An public interface providing a method to determine the distance between a pair of give genes |
| MCScheduling.MixedCriticality.IMixedCriticalitySolver | An interface for an algorithn solving mixed-criticality instances |
| MCScheduling.GeneticAlgorithm.IMutationOperator | Mutation operator is one of the tools of evolution |
| MCScheduling.SimulatedAnnealing.IPerturber< T > | The perturber used to change one state into one of its neighbouring state |
| MCScheduling.GeneticAlgorithm.IRandomizer | A pseudo-random number generator used by a genetic algorithms |
| MCScheduling.GeneticAlgorithm.ISelectionOperator | The selection operator is responsible for selecting an individual chromosomes from the population that will be then used in the further proceedings of the genetic algorithm: recombination phase, namely |
| MCScheduling.MainWindowForm | The main window of the application |
| MCScheduling.ClairvoyantEDF.PriorityQueue< T > | A priority queue implementation based on a binary heap structure, which guarantees that all the operations define has logarithmic time complexity |
| MCScheduling.MixedCriticality.CBaseMixedCriticalitySolver.Status | A status of a solution that is returned as result of the solving algorithm |
| MCScheduling.ClairvoyantEDF.Task | The wrapper of mixed-criticality job to be used with priority queues and criticality queue of the clairvoyant EDF algorithm |
| MCScheduling.Win32 | Auxiliary class that binds its member methods to the same methods of kernel33.dll library |
1.7.4
